a Sic @s6ddlmZddlZddlZddlmZddlmZddlZddl m Z ddl m Z m Z mZmZmZmZmZmZmZmZmZmZddlZddlZddlmZddlmZdd lmZm Z m!Z!m"Z"m#Z#m$Z$dd l%m&Z&m'Z'dd l(m)Z)dd l*m+Z+m,Z,m-Z-m.Z.dd l/m0Z0ddl1m2Z2m3Z3m4Z4m5Z5ddl6m7Z7ddl8m9Z9ddl:m;Z;ddlZ>m?Z?m@Z@ddlAmBZBmCZCmDZDmEZEmFZFddlGmHZHddlImJZJde dKeLeddddZMedQddddd d!d"d#d$d%d%d&d'd(d(d(d)d*d+d,d+d-d!d(d.d/d0d1d2ZNedRd3dddd d!d"d#d$d%d%d&d'd(d(d(d)d*d+d,d+d-d!d(d.d4d0d5d2ZNe.e9d6d7e,d8dd9e-d:d;gd type, default None Data type for data or columns. E.g. {{'a': np.float64, 'b': np.int32}} Use `object` to preserve data as stored in Excel and not interpret dtype. If converters are specified, they will be applied INSTEAD of dtype conversion. engine : str, default None If io is not a buffer or path, this must be set to identify io. Supported engines: "xlrd", "openpyxl", "odf", "pyxlsb". Engine compatibility : - "xlrd" supports old-style Excel files (.xls). - "openpyxl" supports newer Excel file formats. - "odf" supports OpenDocument file formats (.odf, .ods, .odt). - "pyxlsb" supports Binary Excel files. .. versionchanged:: 1.2.0 The engine `xlrd `_ now only supports old-style ``.xls`` files. When ``engine=None``, the following logic will be used to determine the engine: - If ``path_or_buffer`` is an OpenDocument format (.odf, .ods, .odt), then `odf `_ will be used. - Otherwise if ``path_or_buffer`` is an xls format, ``xlrd`` will be used. - Otherwise if ``path_or_buffer`` is in xlsb format, ``pyxlsb`` will be used. .. versionadded:: 1.3.0 - Otherwise ``openpyxl`` will be used. .. versionchanged:: 1.3.0 converters : dict, default None Dict of functions for converting values in certain columns. Keys can either be integers or column labels, values are functions that take one input argument, the Excel cell content, and return the transformed content. true_values : list, default None Values to consider as True. false_values : list, default None Values to consider as False. skiprows : list-like, int, or callable, optional Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file. If callable, the callable function will be evaluated against the row indices, returning True if the row should be skipped and False otherwise. An example of a valid callable argument would be ``lambda x: x in [0, 2]``. nrows : int, default None Number of rows to parse. na_values : scalar, str, list-like, or dict, default None Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values. By default the following values are interpreted as NaN: 'z', 'Fz )subsequent_indenta'. keep_default_na : bool, default True Whether or not to include the default NaN values when parsing the data. Depending on whether `na_values` is passed in, the behavior is as follows: * If `keep_default_na` is True, and `na_values` are specified, `na_values` is appended to the default NaN values used for parsing. * If `keep_default_na` is True, and `na_values` are not specified, only the default NaN values are used for parsing. * If `keep_default_na` is False, and `na_values` are specified, only the NaN values specified `na_values` are used for parsing. * If `keep_default_na` is False, and `na_values` are not specified, no strings will be parsed as NaN. Note that if `na_filter` is passed in as False, the `keep_default_na` and `na_values` parameters will be ignored. na_filter : bool, default True Detect missing value markers (empty strings and the value of na_values). In data without any NAs, passing na_filter=False can improve the performance of reading a large file. verbose : bool, default False Indicate number of NA values placed in non-numeric columns. parse_dates : bool, list-like, or dict, default False The behavior is as follows: * bool. If True -> try parsing the index. * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column. * list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column. * dict, e.g. {{'foo' : [1, 3]}} -> parse columns 1, 3 as date and call result 'foo' If a column or index contains an unparsable date, the entire column or index will be returned unaltered as an object data type. If you don`t want to parse some cells as date just change their type in Excel to "Text". For non-standard datetime parsing, use ``pd.to_datetime`` after ``pd.read_excel``. Note: A fast-path exists for iso8601-formatted dates. date_parser : function, optional Function to use for converting a sequence of string columns to an array of datetime instances. The default uses ``dateutil.parser.parser`` to do the conversion. Pandas will try to call `date_parser` in three different ways, advancing to the next if an exception occurs: 1) Pass one or more arrays (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the string values from the columns defined by `parse_dates` into a single array and pass that; and 3) call `date_parser` once for each row using one or more strings (corresponding to the columns defined by `parse_dates`) as arguments. thousands : str, default None Thousands separator for parsing string columns to numeric. Note that this parameter is only necessary for columns stored as TEXT in Excel, any numeric columns will automatically be parsed, regardless of display format. decimal : str, default '.' Character to recognize as decimal point for parsing string columns to numeric. Note that this parameter is only necessary for columns stored as TEXT in Excel, any numeric columns will automatically be parsed, regardless of display format.(e.g. use ',' for European data). .. versionadded:: 1.4.0 comment : str, default None Comments out remainder of line. Pass a character or characters to this argument to indicate comments in the input file. Any data between the comment string and the end of the current line is ignored. skipfooter : int, default 0 Rows at the end to skip (0-indexed). convert_float : bool, default True Convert integral floats to int (i.e., 1.0 --> 1). If False, all numeric data will be read in as floats: Excel stores all numbers as floats internally. .. deprecated:: 1.3.0 convert_float will be removed in a future version mangle_dupe_cols : bool, default True Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than 'X'...'X'. Passing in False will cause data to be overwritten if there are duplicate names in the columns. .. deprecated:: 1.5.0 Not implemented, and a new argument to specify the pattern for the names of duplicated columns will be added instead {storage_options} .. versionadded:: 1.2.0 Returns ------- DataFrame or dict of DataFrames DataFrame from the passed in Excel file. See notes in sheet_name argument for more information on when a dict of DataFrames is returned. See Also -------- DataFrame.to_excel : Write DataFrame to an Excel file. DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file. read_csv : Read a comma-separated values (csv) file into DataFrame. read_fwf : Read a table of fixed-width formatted lines into DataFrame. Examples -------- The file can be read using the file name as string or an open file object: >>> pd.read_excel('tmp.xlsx', index_col=0) # doctest: +SKIP Name Value 0 string1 1 1 string2 2 2 #Comment 3 >>> pd.read_excel(open('tmp.xlsx', 'rb'), ... sheet_name='Sheet3') # doctest: +SKIP Unnamed: 0 Name Value 0 0 string1 1 1 1 string2 2 2 2 #Comment 3 Index and header can be specified via the `index_col` and `header` arguments >>> pd.read_excel('tmp.xlsx', index_col=None, header=None) # doctest: +SKIP 0 1 2 0 NaN Name Value 1 0.0 string1 1 2 1.0 string2 2 3 2.0 #Comment 3 Column types are inferred but can be explicitly specified >>> pd.read_excel('tmp.xlsx', index_col=0, ... dtype={{'Name': str, 'Value': float}}) # doctest: +SKIP Name Value 0 string1 1.0 1 string2 2.0 2 #Comment 3.0 True, False, and NA values, and thousands separators have defaults, but can be explicitly specified, too. Supply the values you would like as strings or lists of strings! >>> pd.read_excel('tmp.xlsx', index_col=0, ... na_values=['string1', 'string2']) # doctest: +SKIP Name Value 0 NaN 1 1 NaN 2 2 #Comment 3 Comment lines in the excel input file can be skipped using the `comment` kwarg >>> pd.read_excel('tmp.xlsx', index_col=0, comment='#') # doctest: +SKIP Name Value 0 string1 1.0 1 string2 2.0 2 None NaN .z str | intint | Sequence[int] | Nonezlist[str] | NonezHint | str | Sequence[int] | Sequence[str] | Callable[[str], bool] | None bool | NoneDtypeArg | Nonez3Literal['xlrd', 'openpyxl', 'odf', 'pyxlsb'] | Nonez0dict[str, Callable] | dict[int, Callable] | NoneIterable[Hashable] | None4Sequence[int] | int | Callable[[int], object] | None int | Noneboollist | dict | boolCallable | None str | Nonestrintrr&) sheet_nameheadernames index_colusecolssqueezedtypeengine converters true_values false_valuesskiprowsnrowskeep_default_na na_filterverbose parse_dates date_parser thousandsdecimalcomment skipfooter convert_floatmangle_dupe_colsstorage_optionsreturncCsdSNiorBrCrDrErFrGrHrIrJrKrLrMrN na_valuesrOrPrQrRrSrTrUrVrWrXrYrZr]r]Q/var/www/html/django/DPS/env/lib/python3.9/site-packages/pandas/io/excel/_base.py read_exceljs$rbzlist[IntStrT] | Nonezdict[IntStrT, DataFrame]cCsdSr\r]r^r]r]rarbs$rZrZrY) old_arg_name new_arg_namer_rBz2.0) allowed_argsversionTF.z str | int | list[IntStrT] | Nonez$DataFrame | dict[IntStrT, DataFrame]cCsd}t|ts"d}t|||d}n|r8||jkr8tdzJ|j|||||||| | | | | ||||||||||||d}W|r|n|r|0|S)NFT)rZrIz_Engine should not be specified when passing an ExcelFile - ExcelFile already has the engine set)rBrCrDrErFrGrHrJrKrLrMrNr`rOrPrQrRrSrTrUrVrWrXrY) isinstance ExcelFilerI ValueErrorparseclose)r_rBrCrDrErFrGrHrIrJrKrLrMrNr`rOrPrQrRrSrTrUrVrWrXrYrZ should_closedatar]r]rarbsP'   c@seZdZd;dddddZeejddZejd d Zdd d d Z eejdd ddZ ejddddZ ejddddZ ejdBaseExcelReaderNrNone)rZr[cCst|trt|}t|ddid|_t|t|jfsFt|d|dd|_t|jj|jrb|jj|_ nXt |jjdr|jj dz| |jj|_ Wqt y|Yq0ntddS) Nmethod)handle compressionrbFrZis_textreadrzCMust explicitly set engine if not passing in buffer or path for io.)ribytesrr)handlesrj_workbook_classr*rsbookhasattrseek load_workbook Exceptionrmrk)selffilepath_or_bufferrZr]r]ra__init__ s*    zBaseExcelReader.__init__cCsdSr\r]rr]r]rar{*szBaseExcelReader._workbook_classcCsdSr\r])rrr]r]rar/szBaseExcelReader.load_workbookr[cCsFt|dr8t|jdr"|jnt|jdr8|j|jdS)Nr|rmrelease_resources)r}r|rmrrzrr]r]rarm3s      zBaseExcelReader.closez list[str]cCsdSr\r]rr]r]ra sheet_names@szBaseExcelReader.sheet_namesr@)namecCsdSr\r]rrr]r]raget_sheet_by_nameEsz!BaseExcelReader.get_sheet_by_namerA)indexcCsdSr\r])rrr]r]raget_sheet_by_indexIsz"BaseExcelReader.get_sheet_by_indexr<r;)rXrowscCsdSr\r])rsheetrXrr]r]raget_sheet_dataMszBaseExcelReader.get_sheet_data)rr[cCs,t|j}||kr(td|d|ddS)NzWorksheet index z is invalid, z worksheets found)lenrrk)rrZn_sheetsr]r]raraise_if_bad_sheet_by_indexQs  z+BaseExcelReader.raise_if_bad_sheet_by_index)rr[cCs||jvrtd|ddS)NzWorksheet named 'z ' not found)rrkrr]r]raraise_if_bad_sheet_by_nameXs z*BaseExcelReader.raise_if_bad_sheet_by_namer)rM rows_to_user[cCs.d}d}||kr*||s |d7}|d7}q|S)a Determine how many file rows are required to obtain `nrows` data rows when `skiprows` is a function. Parameters ---------- skiprows : function The function passed to read_excel by the user. rows_to_use : int The number of rows that will be needed for the header and the data. Returns ------- int rr])rrMriZrows_used_so_farr]r]ra_check_skiprows_func\s z$BaseExcelReader._check_skiprows_funcr6r:)rCrErMrNr[cCs|dur dS|durd}n2t|r6tt|}d|}ntt|}d|d}t|rz|durztt|}t|dkrz|d7}|dur||St|rtt|}|||St|rdddddd }tt|}|t||||St|r||||SdS) a If nrows specified, find the number of rows needed from the file, otherwise return None. Parameters ---------- header : int, list of int, or None See read_excel docstring. index_col : int, list of int, or None See read_excel docstring. skiprows : list-like, int, callable, or None See read_excel docstring. nrows : int or None See read_excel docstring. Returns ------- int or None NrrrAr<)rMxr[cSs||vSr\r])rMrr]r]rafsz%BaseExcelReader._calc_rows..f) r$rrArr%rrrcallable)rrCrErMrNZ header_rowsrr]r]ra _calc_rowsys8         zBaseExcelReader._calc_rowsrFrhT(str | int | list[int] | list[str] | Noner7r8r9r=r>r?)rBrCrErGrHrKrLrMrNrQrRrSrTrUrVrWrXrYc+ s||durd}ntjdttdt|td| d}t|trJ|}d}n,|dur^|j}d}nt|t rp|g}n|g}t t t t t t ftt|}i}|D]}| rtd|t|t r||}n ||}|||| | }||||}t|dr|t|}|s*t||<qd}d}t|rbt|tsLJd}t|dkrbd}|rzt tt |d }d} |durRt|rRt|tsJg} dgt|d |D]}!t| rt| t sJ|!| 7}!|!t|dkrtd |!d t|dd t||!\||!<|durt ||!|\}"}#| !|"qd}$|r|s|durt|t r|gnt|tsJ|t|tsJt|t|kr|t|}%fd dt"|%D}&t#dd|&D}$t|r|durd }'n"t|t rd|}'n dt$|}'|$r6|'d7}'|'t|krt|tsTJ|D]j}(||'|(})t%|'dt|D]D}!||!|(dks||!|(dur|)||!|(<n ||!|(})qzqXzt&|f||||$|||| | | | d||||||||d|}*|*j'| d||<|r&t||trB| rB||j()| ||_(Wqt*ybt||<Yq0q|rp|S||SdS)NTzDconvert_float is deprecated and will be removed in a future version. stacklevelrNFzReading sheet rmrrz header index z exceeds maximum index z of data.cs$g|]\}}|s|vr|qSr]r]).0rrZ control_rowZindex_col_listr]ra <sz)BaseExcelReader.parse..css|]}|dkp|duVqdS)Nr])rrr]r]ra Az(BaseExcelReader.parse..r)rDrCrEhas_index_namesrGrHrKrLrMrNr`Zskip_blank_linesrRrSrTrUrVrWrFrY)rN)+warningswarn FutureWarningr!r,r3rilistrr@rrr rAdictfromkeyskeysprintrrrrr}rmr0r&r%rrr$rkr-r1append enumerateallmaxranger2rxcolumns set_namesr)+rrBrCrDrErFrGrHrKrLrMrNr`rQrRrSrTrUrVrWrXrYkwdsZret_dictsheetsoutputZ asheetnamerZfile_rows_neededroZis_list_headerZis_len_one_list_header header_namesrow header_name_rZpotential_index_namesZpotential_dataoffsetcollastparserr]rrarls   (                   $zBaseExcelReader.parse)N)N)rrNNNNNNNNNNFFNNrhNrNT)__name__ __module__ __qualname__rpropertyabcabstractmethodr{rrmrrrrrrrrrlr]r]r]rarp sV   Brp) metaclassc @seZdZUdZded<ded<dQdd d d d dd d d dd ddZdZeddddZeddddZ ee j ddddZ ee j ddZ e je j ddddZ dRd ddddd d!d"Ze j dSd ddddd d#d$Zddd%d&Ze j ddd'd(ZdTd d d d dd d d)dd* d+d,Zdd-d.d/Zddd0d1Zeddd2d3Zeddd4d5Zeddd6d7Zed8d9Zed:dd;d<Zed=d>Zddd?d@Zd ddAdBdCZdDddEdFZeddGdHdIdJZ dddKdLZ!dddMdNZ"dddOdPZ#dS)U ExcelWritera\ Class for writing DataFrame objects into excel sheets. Default is to use: * `xlwt `__ for xls files * `xlsxwriter `__ for xlsx files if xlsxwriter is installed otherwise `openpyxl `__ * `odswriter `__ for ods files See ``DataFrame.to_excel`` for typical usage. The writer should be used as a context manager. Otherwise, call `close()` to save and close any opened file handles. Parameters ---------- path : str or typing.BinaryIO Path to xls or xlsx or ods file. engine : str (optional) Engine to use for writing. If None, defaults to ``io.excel..writer``. NOTE: can only be passed as a keyword argument. .. deprecated:: 1.2.0 As the `xlwt `__ package is no longer maintained, the ``xlwt`` engine will be removed in a future version of pandas. date_format : str, default None Format string for dates written into Excel files (e.g. 'YYYY-MM-DD'). datetime_format : str, default None Format string for datetime objects written into Excel files. (e.g. 'YYYY-MM-DD HH:MM:SS'). mode : {{'w', 'a'}}, default 'w' File mode to use (write or append). Append does not work with fsspec URLs. {storage_options} .. versionadded:: 1.2.0 if_sheet_exists : {{'error', 'new', 'replace', 'overlay'}}, default 'error' How to behave when trying to write to a sheet that already exists (append mode only). * error: raise a ValueError. * new: Create a new sheet, with a name determined by the engine. * replace: Delete the contents of the sheet before writing to it. * overlay: Write contents to the existing sheet without removing the old contents. .. versionadded:: 1.3.0 .. versionchanged:: 1.4.0 Added ``overlay`` option engine_kwargs : dict, optional Keyword arguments to be passed into the engine. These will be passed to the following functions of the respective engines: * xlsxwriter: ``xlsxwriter.Workbook(file, **engine_kwargs)`` * openpyxl (write mode): ``openpyxl.Workbook(**engine_kwargs)`` * openpyxl (append mode): ``openpyxl.load_workbook(file, **engine_kwargs)`` * odswriter: ``odf.opendocument.OpenDocumentSpreadsheet(**engine_kwargs)`` .. versionadded:: 1.3.0 **kwargs : dict, optional Keyword arguments to be passed into the engine. .. deprecated:: 1.3.0 Use engine_kwargs instead. Notes ----- For compatibility with CSV writers, ExcelWriter serializes lists and dicts to strings before writing. Examples -------- Default usage: >>> df = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"]) # doctest: +SKIP >>> with pd.ExcelWriter("path_to_file.xlsx") as writer: ... df.to_excel(writer) # doctest: +SKIP To write to separate sheets in a single file: >>> df1 = pd.DataFrame([["AAA", "BBB"]], columns=["Spam", "Egg"]) # doctest: +SKIP >>> df2 = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"]) # doctest: +SKIP >>> with pd.ExcelWriter("path_to_file.xlsx") as writer: ... df1.to_excel(writer, sheet_name="Sheet1") # doctest: +SKIP ... df2.to_excel(writer, sheet_name="Sheet2") # doctest: +SKIP You can set the date format or datetime format: >>> from datetime import date, datetime # doctest: +SKIP >>> df = pd.DataFrame( ... [ ... [date(2014, 1, 31), date(1999, 9, 24)], ... [datetime(1998, 5, 26, 23, 33, 4), datetime(2014, 2, 28, 13, 5, 13)], ... ], ... index=["Date", "Datetime"], ... columns=["X", "Y"], ... ) # doctest: +SKIP >>> with pd.ExcelWriter( ... "path_to_file.xlsx", ... date_format="YYYY-MM-DD", ... datetime_format="YYYY-MM-DD HH:MM:SS" ... ) as writer: ... df.to_excel(writer) # doctest: +SKIP You can also append to an existing Excel file: >>> with pd.ExcelWriter("path_to_file.xlsx", mode="a", engine="openpyxl") as writer: ... df.to_excel(writer, sheet_name="Sheet3") # doctest: +SKIP Here, the `if_sheet_exists` parameter can be set to replace a sheet if it already exists: >>> with ExcelWriter( ... "path_to_file.xlsx", ... mode="a", ... engine="openpyxl", ... if_sheet_exists="replace", ... ) as writer: ... df.to_excel(writer, sheet_name="Sheet1") # doctest: +SKIP You can also write multiple DataFrames to a single sheet. Note that the ``if_sheet_exists`` parameter needs to be set to ``overlay``: >>> with ExcelWriter("path_to_file.xlsx", ... mode="a", ... engine="openpyxl", ... if_sheet_exists="overlay", ... ) as writer: ... df1.to_excel(writer, sheet_name="Sheet1") ... df2.to_excel(writer, sheet_name="Sheet1", startcol=3) # doctest: +SKIP You can store Excel file in RAM: >>> import io >>> df = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"]) >>> buffer = io.BytesIO() >>> with pd.ExcelWriter(buffer) as writer: ... df.to_excel(writer) You can pack Excel file into zip archive: >>> import zipfile # doctest: +SKIP >>> df = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"]) # doctest: +SKIP >>> with zipfile.ZipFile("path_to_file.zip", "w") as zf: ... with zf.open("filename.xlsx", "w") as buffer: ... with pd.ExcelWriter(buffer) as writer: ... df.to_excel(writer) # doctest: +SKIP You can specify additional arguments to the underlying engine: >>> with pd.ExcelWriter( ... "path_to_file.xlsx", ... engine="xlsxwriter", ... engine_kwargs={{"options": {{"nan_inf_to_errors": True}}}} ... ) as writer: ... df.to_excel(writer) # doctest: +SKIP In append mode, ``engine_kwargs`` are passed through to openpyxl's ``load_workbook``: >>> with pd.ExcelWriter( ... "path_to_file.xlsx", ... engine="openpyxl", ... mode="a", ... engine_kwargs={{"keep_vba": True}} ... ) as writer: ... df.to_excel(writer, sheet_name="Sheet2") # doctest: +SKIP r@_engineztuple[str, ...]_supported_extensionsNwztype[ExcelWriter]z)FilePath | WriteExcelBuffer | ExcelWriterr?rz4Literal['error', 'new', 'replace', 'overlay'] | Nonez dict | None) clspathrI date_formatdatetime_formatmoderZif_sheet_exists engine_kwargsr[c Ks,| r&|durtdtjdttd|tur"|dusJt|tr|dkrt|trntj |ddd} nd} z.t j d| d d d }|dkrt | d d }Wn6ty} ztd| d| WYd} ~ n d} ~ 00|dkr t j dd d } | dkr tjdttd|dusJt|}t|S)Nz*Cannot use both engine_kwargs and **kwargsz9Use of **kwargs is deprecated, use engine_kwargs instead.rautorrxlsx io.excel.z.writerTsilentwriterrzNo engine for filetype: ''xlwtzio.excel.xls.writeraAs the xlwt package is no longer maintained, the xlwt engine will be removed in a future version of pandas. This is the only engine in pandas that supports writing in the xls format. Install openpyxl and write to an xlsx file instead. You can set the option io.excel.xls.writer to 'xlwt' to silence this warning. While this option is deprecated and will also raise a warning, it can be globally set and the warning suppressed.)rkrrrr!rrir@osrsplitextr get_optionr.KeyErrorr/object__new__) rrrIrrrrZrrkwargsexterrZxls_config_enginer]r]rarZs@   (  zExcelWriter.__new__rcCs|jS)z'Extensions that writer engine supports.)rrr]r]rasupported_extensionssz ExcelWriter.supported_extensionscCs|jS)zName of engine.)rrr]r]rarIszExcelWriter.enginezdict[str, Any]cCsdS)z(Mapping of sheet names to sheet objects.Nr]rr]r]rarszExcelWriter.sheetscCsdS)z Book instance. Class type will depend on the engine used. This attribute can be used to access engine-specific features. Nr]rr]r]rar|szExcelWriter.bookrqcCsdS)zO Set book instance. Class type will depend on the engine used. Nr])rotherr]r]rar|srrAztuple[int, int] | None)rBstartrowstartcol freeze_panesr[cCs|d||||||S)a6 Write given formatted cells into Excel an excel sheet .. deprecated:: 1.5.0 Parameters ---------- cells : generator cell of formatted data to save to Excel sheet sheet_name : str, default None Name of Excel sheet, if None, then use self.cur_sheet startrow : upper left cell row to dump data frame startcol : upper left cell column to dump data frame freeze_panes: int tuple of length 2 contains the bottom-most row and right-most column to freeze write_cells) _deprecate _write_cellsrcellsrBrrrr]r]rars zExcelWriter.write_cellscCsdS)a Write given formatted cells into Excel an excel sheet Parameters ---------- cells : generator cell of formatted data to save to Excel sheet sheet_name : str, default None Name of Excel sheet, if None, then use self.cur_sheet startrow : upper left cell row to dump data frame startcol : upper left cell column to dump data frame freeze_panes: int tuple of length 2 contains the bottom-most row and right-most column to freeze Nr]rr]r]rarszExcelWriter._write_cellscCs|d|S)zG Save workbook to disk. .. deprecated:: 1.5.0 save)r_saverr]r]rars zExcelWriter.savecCsdS)z( Save workbook to disk. Nr]rr]r]rarszExcelWriter._savezdict[str, Any] | None) rrIrrrrZrrr[c Kst|tr$tj|d} || d|vr4|d7}|dd}ttt t |ddid|_ t|t sxt |||dd|_ d|_|durd |_n||_|durd |_n||_||_|d vrtd |d |rd|vrtd|durd}||_dS)Nrbazr+rt)rtFrvz YYYY-MM-DDzYYYY-MM-DD HH:MM:SS)Nerrornewreplaceoverlayrz^' is not valid for if_sheet_exists. 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May be a URL. {storage_options} Returns ------- str or None Format of file if it can be determined. Raises ------ ValueError If resulting stream is empty. BadZipFile If resulting stream does not have an XLS signature and is not a valid zipfile. ruFrvrNzstream is emptyc3s|]}|VqdSr\)r)rsigpeekr]rarrz'inspect_excel_format..xlscSsg|]}|ddqS)\/)rlower)rrr]r]rar sz(inspect_excel_format..zxl/workbook.xmlrzxl/workbook.binZxlsbz content.xmlZodszip)riryrr*rsr~rx PEEK_SIZErkrXLS_SIGNATURESr ZIP_SIGNATUREzipfileZipFilenamelist)rrZrsstreambufzfZcomponent_namesr]r rainspect_excel_formats:       $   r0c@seZdZUdZddlmZddlmZddlm Z ddl m Z e eee dZ de d <d0d d d dddZddZd1ddddddddddd d ddddd d!d"Zed#d$Zed%d&Zd d'd(d)Zdd'd*d+Zd d'd,d-Zd d'd.d/Zd S)2rjaU Class for parsing tabular excel sheets into DataFrame objects. See read_excel for more documentation. Parameters ---------- path_or_buffer : str, bytes, path object (pathlib.Path or py._path.local.LocalPath), a file-like object, xlrd workbook or openpyxl workbook. If a string or path object, expected to be a path to a .xls, .xlsx, .xlsb, .xlsm, .odf, .ods, or .odt file. engine : str, default None If io is not a buffer or path, this must be set to identify io. Supported engines: ``xlrd``, ``openpyxl``, ``odf``, ``pyxlsb`` Engine compatibility : - ``xlrd`` supports old-style Excel files (.xls). - ``openpyxl`` supports newer Excel file formats. - ``odf`` supports OpenDocument file formats (.odf, .ods, .odt). - ``pyxlsb`` supports Binary Excel files. .. versionchanged:: 1.2.0 The engine `xlrd `_ now only supports old-style ``.xls`` files. 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